Abstract
Osteosarcoma is the most common primary malignant bone tumor, accounting for approximately 20% of all primary malignant bone tumors, and predominantly affects adolescents. The current standard treatment involves a multimodal approach combining neoadjuvant chemotherapy, surgical resection, and postoperative adjuvant chemotherapy. However, patient responses to chemotherapy vary significantly, with response rates (defined as patients achieving ≥90% tumor necrosis) ranging from 30% to 60%. Chemotherapy sensitivity is one of the most critical prognostic factors, and this heterogeneity underscores the importance of predictive tools for optimizing individualized treatment and improving clinical outcomes. In recent years, radiomics has emerged as a revolutionary paradigm in medical imaging analysis. By extracting high-throughput, deep-layer feature information from medical images, it provides a novel technical pathway for quantitative tumor phenotyping. Advanced computer vision algorithms enable the automated extraction of thousands of quantitative metrics-including morphological (shape features), intensity (first-order statistics), and texture (second- and higher-order features)-from multimodal imaging data such as Computed Tomography (CT), Magnetic Resonance Imaging (MRI) and 18F-Fluorodeoxyglucose Positron Emission Tomography (18F-FDG PET/CT) These features not only precisely characterize tumor heterogeneity and the microenvironment but also overcome the subjectivity and reproducibility limitations of traditional manual image interpretation. Leveraging these advantages, radiomics has demonstrated significant value in predicting neoadjuvant chemotherapy efficacy in osteosarcoma.